As methods of classifying or recognizing pieces of information, generally, a method in which matrix information groups are subjected to orthogonal decomposition to find an optimal solution using a plurality of matrix information groups, Baum-Welch algorithm as a method of likelihood estimation, or an algorithm for mathematically calculating an optimal solution such as minimum error classification, has been used.
Further, a method has been known in which a neural network is corrected such that Mahalanobis distance is maintained at an arbitrary, constant distance, when an optimal value of an intermediate layer in the neural network is to be calculated (see, for example, Japanese Patent Laying-Open No. 2003-76976 (hereinafter referred to as “Patent Document 1”).
As a method of vector quantization, a method referred to as K-means has been known, in which an arbitrary center of gravity is applied to a population and recursive classification is continued until the center of gravity reaches an optimal position.
Patent Document 1 discloses a method of optimizing boundary conditions by maintaining Mahalanobis distance constant.
Further, as a method of dividing mixture distribution, expectation maximization, referred to as EM algorithm, has been known in which local solutions are continuously changed to find a local optimal solution in inductive manner, based on frequency distribution of sample appearance and likelihood distribution.
As another method of dividing mixture distribution, a method referred to as support vector machine has been known. According to this method, a non-linear map of a population is transformed to a space of different dimension using an arbitrary function, to determine boundary condition and boundary width.
According to an article “An Estimation of Data Distribution with a Neural Network Model Based on Bayesian Estimation”, Fukashi KOJYO and Hiroshi WAKUYA, material of Technical Society of Measurement, Institute of Electrical Engineers of Japan, October 2003, IM-03-55, pp. 13-18 (hereinafter referred to as Non-Patent Document 1), evaluation for estimating mean and variance as well as standard deviation of a population is performed in accordance with Bayes method, by evaluating whether each sample position is within a specific range of standard deviation or not from the center of gravity of the entire population. Further, “Analysis of Cepstral Features of Japanese Spontaneous Speech Using Mahalanobis Distance”, Masanobu NAKAMURA, Koji IWANO and Sadaoki FURUI, paper of 2005 Spring Meeting of The Acoustical Society of Japan, March 2005, vol. 1, 2-1-14, pp. 231-232 (hereinafter referred to as Non-Patent Document 2) describes high accuracy of phoneme evaluation using Mahalanobis distance.
Methods involving division of mixture distribution and vector quantization as described above have been generally used.
Patent Document 1: Japanese Patent Laying-Open No. 2003-76976
Non-Patent Document 1: “An Estimation of Data Distribution with a Neural Network Model Based on Bayesian Estimation”, Fukashi KOJYO and Hiroshi WAKUYA, material of Technical Society of Measurement, Institute of Electrical Engineers of Japan, October 2003, IM-03-55, pp. 13-18.
Non-Patent Document 2: “Analysis of Cepstral Features of Japanese Spontaneous Speech Using Mahalanobis Distance”, Masanobu NAKAMURA, Koji IWANO and Sadaoki FURUI, paper of 2005 Spring Meeting of The Acoustical Society of Japan, March 2005, vol. 1, 2-1-14, pp. 231-232.